New approach uses graphs for sign language recognition.
problem Challenges in recognizing sign language for deaf individuals.
method Spatial-Temporal Graph Convolutional Network.
result Improved sign language recognition using human skeletal movements.
This paper presents a novel method for structural data recognition using a large number of graph models. In general, prevalent methods for structural data recognition have two shortcomings: 1) Only a single model is used to capture structural variation. 2) Naive recognition methods are used, such as the nearest neighbo…
Transform classical network structures to graph CNN for better graph recognition.
problem Transforming classical network structures to graph CNN for better graph recognition.
method Review and introduce ResNet, Inception, and DenseNet into graph CNN, constructing G_ResNet, G_Inception, G_DenseNet.
result Demonstrated how different network structures work on graph CNN in the graph recognition task.
ChemGrapher uses deep learning to automatically convert chemical compound images into accurate graphs.
problem Automatically converting chemical compound images into accurate graphs with correct bond multiplicity and stereochemical information.
method Developed a deep neural network model for optical compound recognition, including segmentation and classification models.
result Significant error reductions in bond multiplicity and stereochemical information compared to existing tools.
DGP learns speech recognition by modeling complex relationships between utterances.
problem Modeling complex relationships in speech recognition without relational data.
method Bayesian nonparametric deep learning method (DGP) that generates infinite probabilistic graphs.
result DGP successfully infers relationships among utterances without relational data during training.
Enhances graph-based pattern recognition by preserving structural information.
problem Loss of structural information in graph embedding.
method Hierarchical structure construction and Stochastic Graphlet Embedding (SGE).
result Improved graph representation with robustness.
ActiLabel learns activity patterns across diverse sensor devices.
problem Limited adoption of activity recognition models across different domains due to diverse sensor devices.
method Combination of graph model and optimal tiered mapping for learning activity labels.
result Superior performance compared to state-of-the-art methods on public datasets.
New SRC algorithm for faster image recognition on graphs.
problem Image recognition on graphs with subspace assumptions.
method Sparse representation classifier with screening for graph classification.
result Consistent classification for random graphs, faster than original SRC.
Proposes GIB for recognizing informative subgraphs in graphs.
problem Recognizing a subgraph that is maximally informative yet compressive.
method Graph Information Bottleneck (GIB) framework, mutual information estimator, bi-level optimization, connectivity loss.
result IB-subgraph improves graph classification, interpretation, and denoising.
System helps engineers with concept recognition for SEVA.
problem Recognizing systems engineering concepts for SEVA.
method Token classification task, domain expert labeling, pre-trained model fine-tuning, essential datasets creation, knowledge graph construction.
result System successfully recognizes systems engineering concepts.
New graph convolution captures local features on non-Euclidean grids.
problem Capturing local features on irregular, coarse non-Euclidean grids.
method Low-rank learnable local filters in graph convolutions.
result Proves more expressive than previous spectral graph convolution methods.
Two ML approaches compare in recognizing tables from historical records.
problem Recognizing rows and columns in hand-written registry books.
method Comparison of Conditional Random Field and Graph Convolutional Network.
result Both ML methods achieve an 89 F1 score for table detection.
Proposes GIC for graph convolution, improving graph classification.
problem Graphs lack local convolution kernels like images.
method GIC framework using edge-induced and vertex-induced Gaussian mixtures.
result GIC achieves state-of-the-art results on graph classification.
In a real-world setting, visual recognition systems can be brought to make predictions for images belonging to previously unknown class labels. In order to make semantically meaningful predictions for such inputs, we propose a two-step approach that utilizes information from knowledge graphs. First, a knowledge-graph r…
Dynamic time warping applied to temporal graphs for pattern recognition.
problem Comparing temporal graphs using a proximity measure.
method Dynamic Time Warping (DTGW) on temporal graphs.
result DTGW is a flexible measure for temporal graph comparison, NP-hard in general but with polynomial-time solvable special cases.
A new method for neural network initialization using graph degeneracy.
problem Improving neural network performance through better initialization.
method Adapted k-hypercore decomposition for neural network initialization.
result k-hypercore outperforms state-of-the-art initialization methods.
We prove that the homeomorphism problem for 2-manifolds can be decided in logspace. The proof relies on Reingold's logspace solution to the undirected s,t-connectivity problem in graphs.
Algorithm determines spatial graph isomorphism with vertex, edge colorings and orientations.
problem Algorithmic recognition of spatial graphs with various colorings and orientations.
method Proved existence of an algorithm for isomorphic spatial graphs, decomposed into canonical blocks, and applied Haken and Matveev's result.
result Algorithmic recognition of spatial graphs with colorings and orientations.
GraphShield uses dynamic graph learning to detect and visualize financial risks.
problem Detecting and mitigating risks in financial networks.
method Enhanced Cross-Domain Information Learning, Advanced Risk Recognition, Risk Propagation Visualization.
result GraphShield effectively identifies and visualizes hidden financial risks.
In this paper, we tackle the problem of online semi-supervised learning (SSL). When data arrive in a stream, the dual problems of computation and data storage arise for any SSL method. We propose a fast approximate online SSL algorithm that solves for the harmonic solution on an approximate graph. We show, both empiric…
Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams. However, these methods assume the existence of fully-labelled training data, which is unrealistic for most real-world applications. We present a novel appro…
Graph autoencoders enable ML across diverse sensor networks.
problem Deploying ML across different sensor networks with varying types or layouts.
method Graph Autoencoders for activity recognition across heterogeneous sensor networks.
result Transferable activity classifiers achieve 75% accuracy on unseen sensor layouts.
The family of image visibility graphs (IVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibilit…
It is a usual practice to ignore any structural information underlying classes in multi-class classification. In this paper, we propose a graph convolutional network (GCN) augmented neural network classifier to exploit a known, underlying graph structure of labels. The proposed approach resembles an (approximate) infer…
This survey article describes the algorithmic approaches successfully used over the time to construct hyperbolic structures on 3-dimensional topological "objects" of various types, and to classify several classes of such objects using such structures.
Graph-based multimodal federated learning for HAR improves accuracy and privacy.
problem Challenges in HAR due to noisy data, incomplete measurements, and privacy concerns.
method Proposes GraMFedDHAR, a Graph-based Multimodal Federated Learning framework for HAR tasks, using modality-specific graphs, residual GCNs, and attention-based fusion.
result Experimental results show up to 13 percent performance improvement for MultiModalGCN under differential privacy constraints.
Survey on methods to learn graph data representations.
problem Designing optimal Neural Network architectures for arbitrary graphs.
method Review of graph kernel methods, convolutional approaches, graph neural networks, graph embedding, and probabilistic approaches.
result Discussion of various methods for learning graph data representations.
IGT learns graph representations without supervision.
problem Building deep unsupervised graph representations.
method Generic complex-valued spectral graph architecture from Fourier transform generalization, greedy concave objective for discriminative and invariant features.
result IGT learns both discriminative and invariant features from graph topology.
Framework for differentiating WFSTs for structured loss functions.
problem Training dynamic structured loss functions in neural networks.
method Automatic differentiation with WFSTs, combining pruning and back-off.
result Demonstrated learning over WFST latent phrase decomposition.
New speech recognition method uses hypergraphs for better label prediction.
problem Missed information in pairwise relationships between speech samples.
method Hypergraph Laplacian based semi-supervised learning methods applied to speech recognition.
result Sensitivity performance measures of hypergraph methods are better than state-of-the-art methods.
Sparsity-based representations have recently led to notable results in various visual recognition tasks. In a separate line of research, Riemannian manifolds have been shown useful for dealing with features and models that do not lie in Euclidean spaces. With the aim of building a bridge between the two realms, we addr…
Survey on deep learning for malware classification, including unknown threats.
problem Classifying and recognizing unknown malware variants.
method Review of deep learning techniques and OSR solutions.
result Deep learning can effectively classify known malware and recognize unknown threats.
Topology speeds up and improves CNNs for digit recognition.
problem Improving and speeding up deep learning models for digit recognition.
method Topological data analysis applied to CNNs to modify computations.
result Topology improves CNNs' performance and generalization.
Paper converts graph learning to lifelong learning.
problem Learning graphs in a streaming fashion.
method Feature graph topology, converting node classification to graph classification.
result FGN achieves superior performance in lifelong human action recognition and feature matching.
Study on Frechet distance properties for paths and graphs.
problem Understanding topological properties of Frechet distance spaces.
method Proving path-connectedness of Frechet distance spaces and metric balls.
result Spaces of paths and graphs under Frechet distance are path-connected.
Domain adaptation (DA) addresses the real-world image classification problem of discrepancy between training (source) and testing (target) data distributions. We propose an unsupervised DA method that considers the presence of only unlabelled data in the target domain. Our approach centers on finding matches between sa…
Survey on graph neural networks for non-Euclidean data.
problem Challenges in processing graph data.
method Taxonomy of graph neural networks.
result Comprehensive overview and taxonomy of GNNs.
Contrastive examples improve fairness in face recognition by balancing minority and majority groups.
problem Face recognition algorithms favor majority groups in training data.
method Create contrastive examples by swapping group memberships in the training dataset.
result Contrastive examples improve fairness metrics like equalized odds.
Deep learning improves community detection in graph datasets.
problem Community detection in graph datasets using deep learning.
method Proposes a deep learning approach using Gumbel Softmax for clustering graph nodes.
result The new approach significantly outperforms traditional clustering methods.
Combines SBMs and graph neural nets for graph embeddings.
problem Discovering community structure and link prediction on graphs.
method Sparse variational autoencoder integrating SBMs and graph neural nets.
result Encouraging link prediction results with interpretable latent structure.
Mapper-GIN simplifies 3D point cloud classification with lightweight structure.
problem Robust 3D point cloud classification under corruption.
method Mapper algorithm for structural decomposition, GIN for graph classification.
result Mapper-GIN achieves competitive accuracy with minimal parameters.
PSimGNN partitions graphs into subgraphs for efficient graph similarity computation.
problem Efficiently compute graph similarity scores for large graphs.
method Graph partitioning followed by subgraph-level and node-level comparisons using a graph neural network.
result PSimGNN outperforms state-of-the-art methods in graph similarity computation tasks.
This study bridges the gap between spatial and spectral GNNs.
problem Lack of direct comparison and cross-reference of existing GNNs.
method Systematically categorizes and examines GNNs into spatial and spectral domains.
result Establishes a strong relationship between spatial and spectral GNNs.
EvoNet predicts events in time-series data by evolving state graphs.
problem Predicting events in time-series data with interpretable patterns.
method Evolutionary State Graph (ESG) and EvoNet model.
result EvoNet outperforms baselines and provides insights into event predictions.
Graph filtering framework improves semi-supervised learning efficiency.
problem Efficiently leverage unlabeled data with limited labeled data.
method Graph filtering to inject graph similarity into data features.
result Unified insights and improved modeling capabilities of label propagation and graph convolutional networks.
A new HGNN framework for complex data representation learning.
problem Learning representation for complex data in hypergraph structures.
method Designing hyperedge convolution operations for hypergraph learning.
result HGNN outperforms state-of-the-art methods in citation network and visual object recognition tasks.
Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semi-structured data as graphs where nodes correspond to primitives (parts, interest points, segments, etc.) and edges characterize the relationships between these primitives. However, these …
Nonnegative Matrix Factorization (NMF) has been continuously evolving in several areas like pattern recognition and information retrieval methods. It factorizes a matrix into a product of 2 low-rank non-negative matrices that will define parts-based, and linear representation of nonnegative data. Recently, Graph regula…